def _get_ef(self):
"""If self.ef does not exist, create and return an instance of
finquant.efficient_frontier.EfficientFrontier, else, return the
existing instance.
"""
if self.ef is None:
# create instance of EfficientFrontier
self.ef = EfficientFrontier(
self.comp_mean_returns(freq=1),
self.comp_cov(),
risk_free_rate=self.risk_free_rate,
freq=self.freq,
)
return self.ef
# <codecell> # maximum Sharpe ratio for a given target volatility of 0.22 pf.ef_efficient_volatility(0.22, verbose=True) # <markdowncell> # ### Manually creating an instance of EfficientFrontier # If required, or preferred, the below code shows how the same is achieved by manually creating an instance of EfficientFrontier, passing it the mean returns and covariance matrix of the previously assembled portfolio. # <codecell> from finquant.efficient_frontier import EfficientFrontier # creating an instance of EfficientFrontier ef = EfficientFrontier(pf.comp_mean_returns(freq=1), pf.comp_cov()) # optimisation for minimum volatility print(ef.minimum_volatility()) # <codecell> # printing out relevant quantities of the optimised portfolio (expected_return, volatility, sharpe) = ef.properties(verbose=True) # <markdowncell> # ### Computing and visualising the Efficient Frontier # `FinQuant` offers several ways to compute the *Efficient Frontier*. # 1. Through the opject `pf` # - with automatically setting limits of the *Efficient Frontier* # 2. By manually creating an instance of `EfficientFrontier` and providing the data from the portfolio
# performs and plots results of Monte Carlo run (5000 iterations)
opt_w, opt_res = pf.mc_optimisation(num_trials=5000)
# plots the results of the Monte Carlo optimisation
pf.mc_plot_results()
# plots the Efficient Frontier
pf.ef_plot_efrontier()
# plots optimal portfolios based on Efficient Frontier
pf.ef.plot_optimal_portfolios()
# plots individual plots of the portfolio
pf.plot_stocks()
plt.show()
print(' ')
print('optimised portfolio results')
# creating an instance of EfficientFrontier
ef = EfficientFrontier(pf.comp_mean_returns(freq=1), pf.comp_cov())
# optimisation for minimum volatility
print(ef.minimum_volatility())
# printing out relevant quantities of the optimised portfolio
(expected_return, volatility, sharpe) = ef.properties(verbose=True)
results = str(ef.minimum_volatility())
y = input("If you want to save results then say 'y' :" )
if y in ['y', 'Y', 'yes', 'Yes', 'YES']:
text_file = open('output.txt', 'w')
text_file.write(results)
text_file.close()
print('saved in output.txt')